Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2 + NO3, SiO2 and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO4 are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO2 in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.